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Machine Learning Scientist - Agents for Applied Small Molecule Drug Design - Genentech
ML Engineer
Senior Machine Learning Scientist leading the design of autonomous, LLM‑driven agentic workflows that integrate ML, physics‑based methods, and cheminformatics to accelerate small‑molecule drug discovery.
About the role
Key Responsibilities
- Design, develop, and deploy autonomous agentic workflows that orchestrate large language models, machine learning pipelines, and physics‑based simulations for small‑molecule drug design.
- Collaborate with chemists and structural biologists to translate scientific challenges into computational solutions and iterate on model performance.
- Fine‑tune and benchmark foundation models on domain‑specific data, ensuring robustness and interpretability.
- Integrate cheminformatics tools and molecular simulation engines into end‑to‑end pipelines, optimizing for speed and accuracy.
- Document architecture, conduct code reviews, and maintain reproducible research artifacts.
Requirements
- PhD or equivalent experience in Machine Learning, Computational Chemistry, or related field.
- Proven expertise in large language models, autonomous agent design, and deep learning frameworks (PyTorch/TensorFlow).
- Strong programming skills in Python and experience with cheminformatics libraries (RDKit, OpenEye).
- Knowledge of physics‑based modeling techniques (molecular dynamics, quantum chemistry) is highly desirable.
- Excellent communication skills and ability to work cross‑functionally in a fast‑paced research environment.